Implementation of Custom Image Processing Algorithm For 3D Features Generation. For paper titled "Revolutionizing Signature Recognition: A Contactless Approach with Convolutional-Recurrent Neural Network (C-RNN)"
This custom image processing algorithm standardizes and preserves the full sequence of in-air hand gesture signatures. It processes 2000 samples across 100 classes, representing 100 individuals, ensuring the complete preservation of information within 10 frames using MHI and a specialized image processing technique. Each sample, with dimensions 640 x 480 x N, varies in frame count (N). This algorithm uniquely standardizes these varying frame counts, encapsulating all spatio-temporal information within a concise 10-frame format.
To clone this repository and start exploring the this image processing algorithm on your local machine.
git clone https://github.com/alvinlimfangchuen/iHGS-MHI-BLOCKS.git- MATLAB: Compatible with any version, this algorithm was specifically developed in MATLAB 2021a.
- Dependencies: No first-party toolboxes or third-party libraries are required for this algorithm.
The implementation of this project is based on the In-Air Hand Gesture Signature (iHGS) database, which is currently the only publicly available image-based dataset for in-air hand gesture signature recognition. For more information on the iHGS database and to access it for your research, please visit the following link and contact the corresponding author:
In-Air Hand Gesture Signature (iHGS) Database
Please ensure you adhere to the dataset's usage guidelines and cite it appropriately in your work.
@article{lim2024inair,
title={TBC},
author={Alvin Fang Chuen Lim, Wee How Khoh, Ying Han Pang, Hui Yen Yap},
doi = {TBC},
journal={International Journal of Technology},
volume={},
number={},
pages={},
year={2024}
}